Try and practice the latest HP : HPE2-B08 real questions & answers

A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)

• A. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
• B. Retrain the model from scratch using a much larger and more diverse public dataset.
• C. Increase the number of hidden layers in the model's architecture.
• D. Apply stricter content moderation and safety guardrails to the model's output.
• E. Reduce the "temperature" setting during inference to make the model's output less random and more focused.

A customer has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?

• A. 8 worker nodes, 32 total H100 NVL GPUs, dual rack
• B. 2 worker nodes, 8 total L40S GPUs, single rack
• C. 4 worker nodes, 16 total L40S GPUs, single rack
• D. 4 worker nodes, 16 total H100 NVL GPUs, dual rack

During a discovery call, a customer from a telecommunications company explains their primary goal:
"We need to analyze network traffic patterns in real-time to detect anomalies that could indicate a security threat or a network outage." Which key use case for HPE Private Cloud AI does this represent?

• A. Code Generation
• B. Document Chat
• C. AI Recommender System
• D. AI Cybersecurity

An architect is designing a high-performance computing environment for a customer with two distinct, demanding workloads:
1. A large-scale, multi-node deep learning model that requires the fastest possible server-to-server communication.
2. A data-intensive analytics workload that requires the fastest possible data ingestion from an HPE GreenLake for File Storage array.
Which combination of technologies from the HPE Private Cloud AI solution should the architect select to optimize both workloads? (Select all that apply.)

• A. InfiniBand for the server-to-server fabric and Fibre Channel for the storage fabric.
• B. Multi-Instance GPU (MIG) to partition GPUs for each workload.
• C. NVLink for intra-server GPU communication and RoCE for the server-to-server and server-to-storage fabric.
• D. GPUDirect Storage (GDS) to enable a direct data path between storage and GPUs for the analytics workload.
• E. A flat TCP/IP network for simplicity, relying on the speed of the 200GbE adapters.

An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?

• A. HPE Alletra Storage MP with RoCE-capable network adapters
• B. HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
• C. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge
• D. HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs